BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields

TL;DR

This paper introduces a neural radiance fields-based method for reconstructing dynamic, deformable scenes from endoscopic videos without known camera poses, enhancing surgical visualization and robotic intervention capabilities.

Contribution

It presents a novel NeRF-based approach that handles unknown camera poses and scene deformability, improving upon prior static and modular methods in surgical scene reconstruction.

Findings

Successfully reconstructs dynamic surgical scenes from endoscopic videos.

Outperforms existing methods in handling unknown camera poses.

Demonstrates versatility across diverse surgical datasets.

Abstract

Reconstruction of deformable scenes from endoscopic videos is important for many applications such as intraoperative navigation, surgical visual perception, and robotic surgery. It is a foundational requirement for realizing autonomous robotic interventions for minimally invasive surgery. However, previous approaches in this domain have been limited by their modular nature and are confined to specific camera and scene settings. Our work adopts the Neural Radiance Fields (NeRF) approach to learning 3D implicit representations of scenes that are both dynamic and deformable over time, and furthermore with unknown camera poses. We demonstrate this approach on endoscopic surgical scenes from robotic surgery. This work removes the constraints of known camera poses and overcomes the drawbacks of the state-of-the-art unstructured dynamic scene reconstruction technique, which relies on the static part of the scene for accurate reconstruction. Through several experimental datasets, we demonstrate the versatility of our proposed model to adapt to diverse camera and scene settings, and show its promise for both current and future robotic surgical systems.